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app.py

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+# import gradio as gr
+# from huggingface_hub import InferenceClient
+
+# """
+# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+# """
+# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+
+# def respond(
+#     message,
+#     history: list[tuple[str, str]],
+#     system_message,
+#     max_tokens,
+#     temperature,
+#     top_p,
+# ):
+#     messages = [{"role": "system", "content": system_message}]
+
+#     for val in history:
+#         if val[0]:
+#             messages.append({"role": "user", "content": val[0]})
+#         if val[1]:
+#             messages.append({"role": "assistant", "content": val[1]})
+
+#     messages.append({"role": "user", "content": message})
+
+#     response = ""
+
+#     for message in client.chat_completion(
+#         messages,
+#         max_tokens=max_tokens,
+#         stream=True,
+#         temperature=temperature,
+#         top_p=top_p,
+#     ):
+#         token = message.choices[0].delta.content
+
+#         response += token
+#         yield response
+
+# """
+# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+# """
+# demo = gr.ChatInterface(
+#     respond,
+#     additional_inputs=[
+#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+#         gr.Slider(
+#             minimum=0.1,
+#             maximum=1.0,
+#             value=0.95,
+#             step=0.05,
+#             label="Top-p (nucleus sampling)",
+#         ),
+#     ],
+# )
+
+
+# if __name__ == "__main__":
+#     demo.launch()
+
+
+
+
+import gradio as gr
+import torch
+from transformers import RagRetriever, RagSequenceForGeneration, AutoTokenizer
+
+"""
+For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+"""
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def strip_title(title):
+    if title.startswith('"'):
+        title = title[1:]
+    if title.endswith('"'):
+        title = title[:-1]
+    return title
+
+def retrieved_info(rag_model, query):
+    # Tokenize query
+    retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
+        [query],
+        return_tensors="pt",
+        padding=True,
+        truncation=True,
+    )["input_ids"].to(device)
+
+    # Retrieve documents
+    question_enc_outputs = rag_model.rag.question_encoder(retriever_input_ids)
+    question_enc_pool_output = question_enc_outputs[0]
+
+    result = rag_model.retriever(
+        retriever_input_ids,
+        question_enc_pool_output.cpu().detach().to(torch.float32).numpy(),
+        prefix=rag_model.rag.generator.config.prefix,
+        n_docs=rag_model.config.n_docs,
+        return_tensors="pt",
+    )
+
+    # Display retrieved documents including URLs
+    all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
+    retrieved_context = []
+    for docs in all_docs:
+        titles = [strip_title(title) for title in docs["title"]]
+        texts = docs["text"]
+        for title, text in zip(titles, texts):
+            #print(f"Title: {title}")
+            #print(f"Context: {text}")
+            retrieved_context.append(f"{title}: {text}")
+
+    answer = retrieved_context
+    return answer
+
+
+
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens ,
+    temperature,
+    top_p,
+):
+    # Load model
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    dataset_path = "./sample/my_knowledge_dataset"
+    index_path = "./sample/my_knowledge_dataset_hnsw_index.faiss"
+
+    tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq")
+    retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom",
+                                             passages_path = dataset_path,
+                                             index_path = index_path,
+                                             n_docs = 5)
+    rag_model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-nq', retriever=retriever)
+    rag_model.retriever.init_retrieval()
+    rag_model.to(device)
+
+    if message:  # If there's a user query
+        response = retrieved_info(rag_model, message)  # Get the answer from your local FAISS and Q&A model
+        return response[0]
+
+    # In case no message, return an empty string
+    return ""
+
+
+
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+# Custom title and description
+title = "🧠 Welcome to Your AI Knowledge Assistant"
+description = """
+HI!!, I am a chatbot, I retrieves relevant information from a custom dataset using RAG. Ask any question, and let me assist you.
+My capabilities and knowledge is limited right now because of computational resources. Originally I can acess more than a million files 
+from my knowledge-base but, right now, I am limited to less than 1000 files. LET'S BEGGINNNN......
+"""
+
+demo = gr.ChatInterface(
+    respond,
+    type = 'messages',
+    additional_inputs=[
+        gr.Textbox(value="You are a helpful and friendly assistant.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
+    ],
+    title=title,
+    description=description,
+    textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
+    examples=[["✨Future of AI"], ["📱App Development"]],
+    example_icons=["🤖", "📱"],
+    theme="compact",
+)
+
+
+if __name__ == "__main__":
+    demo.launch(share = True )

requirements.txt

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+torch == 2.4.0
+git+https://github.com/huggingface/transformers.git
+faiss-cpu
+datasets